Bulletin of the American Physical Society
2005 APS March Meeting
Monday–Friday, March 21–25, 2005; Los Angeles, CA
Session S7: Gene Chips |
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Sponsoring Units: DBP Chair: Ned Wingreen, Princeton University Room: LACC 408B |
Wednesday, March 23, 2005 2:30PM - 3:06PM |
S7.00001: Gene Chips: A New Tool for Biology Invited Speaker: The knowledge of many complete genomic sequences has led to a ``grand unification of biology,'' consisting of direct evidence that most of the basic cellular functions of all organisms are carried out by genes and proteins whose primary sequences are directly related by descent (i.e. orthologs). Further, genome sequences have made it possible to study all the genes of a single organism simultaneously. We have been using DNA microarrays (sometime referred to as ``gene chips'') to study patterns of gene expression and genome rearrangement in yeast and human cells under a variety of conditions and in human tumors and normal tissues. These experiments produce huge volumes of data; new computational and statistical methods are required to analyze them properly. Examples from this work will be presented to illustrate how genome-scale experiments and analysis can result in new biological insights not obtainable by traditional analyses of genes and proteins one by one. For lymphomas, breast tumors, lung tumors, liver tumors, gastric tumors, brain tumors and soft tissue tumors we have been able, by the application of clustering algorithms, to subclassify tumors of similar anatomical origin on the basis of their gene expression patterns. These subclassifications appear to be reproducible and clinically as well as biologically meaningful. By studying synchronized cells growing in culture, we have identified many hundreds of yeast and human genes that are expressed periodically, at characteristically different points in the cell division cycle. In humans, it turns out that most of these genes are the same genes that comprise the ``proliferation cluster,'' i.e. the genes whose expression is specifically associated with the proliferativeness of tumors and tumor cell lines. Finally, we have been applying a variant of our DNA microarray technology (which we call ``array comparative hybridization'') to follow the DNA copy number of genes, both in tumors and in yeast cells undergoing adaptive evolution during hundreds of generations of growth in continuous culture. These studies suggest a basic similarity in mechanism between adaptive evolution in yeast and tumor progression in humans. [Preview Abstract] |
Wednesday, March 23, 2005 3:06PM - 3:42PM |
S7.00002: Mining gene-chip data Invited Speaker: DNA microarray (``gene chip'') technology has enabled a rapid accumulation of gene-expression data for model organisms such as {\it S. cerevisiae} and {\it C. elegans}, as well as for {\it H. sapiens}, raising the issue of how best to extract information about the gene regulatory networks of these organisms from this data. While basic clustering algorithms have been successful at finding genes that are coregulated for a small, specific set of experimental conditions, these algorithms are less effective when applied to large, varied data sets. One of the major challenges in analyzing the data is the diversity in both size and signal strength of the various transcriptional modules, {\it i.e.} sets of coregulated genes along with the sets of conditions for which the genes are strongly coregulated. One method that has proven successful at identifying large and/or strong modules is the Iterative Signature Algorithm (ISA) [1]. A modified version of the ISA algorithm, the Progressive Iterative Signature Algorithm (PISA), is also able to identify smaller, weaker modules by sequentially eliminating transcriptional modules as they are identified. Applying these algorithms to a large set of yeast gene expression data illustrates the strengths and weaknesses of each approach. [1] Bergmann, S., Ihmels, J., and Barkai, N., Phys. Rev. E {\bf 67}, 031902 (2002). [Preview Abstract] |
Wednesday, March 23, 2005 3:42PM - 4:18PM |
S7.00003: Combining gene-chip data and bioinformatics to define transcription networks Invited Speaker: The development of DNA microarray technology has made it possible to simultaneously monitor the mRNA abundance of all genes (``transcriptome'') for a variety of cellular conditions. In addition, microarrays have been used to map protein-DNA interactions by measuring occupancy profiles along the chromosome for an increasing number of transcription factors (TFs), especially in the yeast S. cerevisiae. With this data and the complete genome sequence on hand, it is becoming possible to quantitatively model the molecular computation performed near the transcription start site of the gene. This computation has as input the nuclear concentrations of the active form of various regulatory proteins (``regulome'') and as output a transcription rate, which together with the half-life of the transcript determines the mRNA abundance. Our laboratory has pioneered the use of multivariate regression methods to link mRNA expression data with genome sequence data and TF occupancy data. This allows us to: (i) discover cis-regulatory elements in non-coding regulatory regions; (ii) infer the condition-dependent regulatory activities of transcription factors as ``hidden variables''; and (iii) accurately determine which genes are controlled by which transcription factors. Together, our results show that model-based analysis of functional genomics data provides a versatile and extensible conceptual and practical framework for the elucidation of regulatory circuitry, and a powerful alternative to the currently popular methods based on clustering and ``modules''. [Preview Abstract] |
Wednesday, March 23, 2005 4:18PM - 4:54PM |
S7.00004: Integrating Genetic and Functional Genomic Data to Elucidate Common Disease Tra Invited Speaker: The reconstruction of genetic networks in mammalian systems is one of the primary goals in biological research, especially as such reconstructions relate to elucidating not only common, polygenic human diseases, but living systems more generally. Here I present a statistical procedure for inferring causal relationships between gene expression traits and more classic clinical traits, including complex disease traits. This procedure has been generalized to the gene network reconstruction problem, where naturally occurring genetic variations in segregating mouse populations are used as a source of perturbations to elucidate tissue-specific gene networks. Differences in the extent of genetic control between genders and among four different tissues are highlighted. I also demonstrate that the networks derived from expression data in segregating mouse populations using the novel network reconstruction algorithm are able to capture causal associations between genes that result in increased predictive power, compared to more classically reconstructed networks derived from the same data. This approach to causal inference in large segregating mouse populations over multiple tissues not only elucidates fundamental aspects of transcriptional control, it also allows for the objective identification of key drivers of common human diseases. [Preview Abstract] |
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